Crowd Density Estimation Using Wireless Sensor Networks

Estimation of crowd distribution is critical to various applications. Although most researches have provided solutions based on images and videos technologies, the high costs for deploying and an over-dependence on the bright light restrict its scope of application. In this paper, we use wireless sensor networks (WSNs) originally to make up for the lack of camera. Our approach is an iterative process which contains two phases in each time slot. In detection step, we divide the crowd density into different levels according to the RSSI data obtained by WSNs using K-means algorithm. In calibration step, we eliminate the noises and other deviations estimation based on the spatial-temporal correlation of crowd distribution. In addition, we have implemented and evaluated our algorithm by extensive real-world experiments using 16 sensor nodes and large-scale simulations. The results show that our algorithm has an accurate, efficient, and consistent performance.

I. Introduction

The ability of automatically detecting the crowd in certain environments is fundamental to various applications. The estimation of crowd density is often widely used in human safety monitoring, traffic control, smart guiding in museum and other significant applications. In order to get a precise distribution, most classical solutions are using images and video to analyze. By the steps of background modeling, changing detection, grouping and event interpretation, they can obtain the distribution of crowd density. However, these approaches are deficient in handling occlusion and crowded scenes, and the costs of complex computing also eliminate the practicability as well.